Journal ArticleDOI
Toward Emotion Aware Computing: An Integrated Approach Using Multichannel Neurophysiological Recordings and Affective Visual Stimuli
Christos A. Frantzidis,Charalampos Bratsas,Christos Papadelis,Evdokimos I. Konstantinidis,Costas Pappas,Panagiotis D. Bamidis +5 more
- Vol. 14, Iss: 3, pp 589-597
TLDR
In this article, the authors proposed a methodology for the robust classification of neurophysiological data into four emotional states collected during passive viewing of emotional evocative pictures selected from the International Affective Picture System.Abstract:
This paper proposes a methodology for the robust classification of neurophysiological data into four emotional states collected during passive viewing of emotional evocative pictures selected from the International Affective Picture System. The proposed classification model is formed according to the current neuroscience trends, since it adopts the independency of two emotional dimensions, namely arousal and valence, as dictated by the bidirectional emotion theory, whereas it is gender-specific. A two-step classification procedure is proposed for the discrimination of emotional states between EEG signals evoked by pleasant and unpleasant stimuli, which also vary in their arousal/intensity levels. The first classification level involves the arousal discrimination. The valence discrimination is then performed. The Mahalanobis (MD) distance-based classifier and support vector machines (SVMs) were used for the discrimination of emotions. The achieved overall classification rates were 79.5% and 81.3% for the MD and SVM, respectively, significantly higher than in previous studies. The robust classification of objective emotional measures is the first step toward numerous applications within the sphere of human-computer interaction.read more
Citations
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Journal ArticleDOI
Feature Extraction and Selection for Emotion Recognition from EEG
TL;DR: This work reviews feature extraction methods for emotion recognition from EEG based on 33 studies, and results suggest preference to locations over parietal and centro-parietal lobes.
Journal ArticleDOI
Emotions Recognition Using EEG Signals: A Survey
TL;DR: A survey of the neurophysiological research performed from 2009 to 2016 is presented, providing a comprehensive overview of the existing works in emotion recognition using EEG signals, and a set of good practice recommendations that researchers must follow to achieve reproducible, replicable, well-validated and high-quality results.
Journal ArticleDOI
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
TL;DR: The proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram (EEG) channels via training a neural network so as to benefit for more discriminative EEG feature extraction.
Journal ArticleDOI
Recognition of emotions using multimodal physiological signals and an ensemble deep learning model
TL;DR: The superiority of the MESAE against the state-of-the-art shallow and deep emotion classifiers has been demonstrated under different sizes of the available physiological instances.
Journal ArticleDOI
Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
TL;DR: The emotion recognition methods based on multi-channel EEG signals as well as multi-modal physiological signals are reviewed and the correlation between different brain areas and emotions is discussed.
References
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